Undergraduate Minor

The Institute for Computational Medicine is proud to offer an Undergraduate Minor in Computational Medicine, the first educational program in CM, reflecting Johns Hopkins University’s leadership in this field. Like the ICM itself, the Undergraduate Minor in Computational Medicine is integrative and multidisciplinary. The 17 ICM Core Faculty who serve as advisors to the Undergraduate Concentration in Computational Medicine hold primary and joint appointments in multiple Johns Hopkins University departments and schools including Biomedical Engineering, Computer Science, Electrical and Computer Engineering, Mechanical Engineering, Applied Mathematics and Statistics (WSE); Neurosurgery, Emergency Medicine, Medicine, and the Divisions of Cardiology and Health Sciences Informatics (SOM); and Health Policy and Management (BSPH).

Undergraduates who are interested in learning about the Minor in Computational Medicine are encouraged to attend the ICM’s annual Computational Medicine Night.

With a minor in CM, students will have a solid grounding in the development and application of computational methods in multiple key areas of medicine. Specifically, they will understand how mathematical models can be constructed from biophysical laws or experimental data, and how predictions from these models facilitate diagnosis and treatment of a disease. Graduating students will be conversant with a wide variety of statistical, deterministic and stochastic modeling methods. They will be able to develop a model and to write code to implement it; they will be able to analyze and visualize the resulting data from the simulations. These skills are essential to the advancement of modern medicine, and are prized both in academic research and industrial research. The courses and research opportunities available in the CM minor will place students at the forefront of the application of mathematics, computing and engineering to human health. Whether you go on to medical school, graduate research, or biomedical industries, the comprehensive quantitative training and exposure to cutting edge CM techniques will give you a competitive advantage for working in the medicine of tomorrow – which will be data-driven, predictive, personalized and preventative.

Yes. The minor will provide both foundational training and opportunities for specialization in Computational Medicine. Students can select electives from an approved list that match their interests. We also provide examples of curricula in some key subareas of Computational Medicine, including:

Computational Physiological Medicine develops mechanistic models of biological systems in disease, and applies the insights gained from these models to develop improved diagnostics and therapies. Therapies could be diverse drugs, electrical stimulation, mechanical support devices and more.

Computational Molecular Medicine harnesses the enormous amount of disease-relevant data produced by next-generation sequencing, microarray and proteomic experiments of large patient cohorts, using statistical models to identify the drivers of disease and the susceptible links in disease networks.

Computational Anatomical Medicine uses medical imaging to analyze the variation in structure of human organs in health and disease. Such image analysis has been integrated into clinical workflows to assist in the diagnosis and prognosis of complex diseases.

Computational Healthcare is an emerging field devoted to understanding populations of patients and their interaction with all aspects of the healthcare process.

Techniques for and applications in each of these four key subareas will be introduced in the required core courses, so that students will be exposed to the breadth of Computational Medicine, and will be able to identify preferred areas of interest.

Before attempting the minor, undergraduates will have taken the following courses. For a course to count towards the minor, a minimum grade of C- is required (courses graded as ‘S/U’ do not satisfy prerequisites):

Calculus I

Calculus II

Probability and Statistics: either a single course covering both (e.g. 553.310 or 553.311), or a course devoted to each (e.g. 553.420 and 553.430) – this may be taken concurrent with Introduction to Computational Medicine (see below).

The required core courses for the minor are Introduction to Computational Medicine I (EN.580.431) and one of the following to be taken in the junior or senior year:

Introduction to Data Science for Biomedical Engineering (EN.580.464) or

Foundations of Computational Biology and Bioinformatics II (EN.580.488) or

Systems Pharmacology & Personalized Medicine (EN.580.430).

EN.580.431 covers computational anatomy and physiology and will be jointly taught by ICM faculty from multiple departments.

EN.580.464 covers the basics of data science in biomedical engineering and requires proficiency in basic programming in at least one language, basic calculus, and linear algebra . EN.580.488 introduces probabilistic modeling and information theory applied to biological sequence analysis and EN.580.430 focuses on the applications of pharmacokinetics and pharmacodynamics to simulating the effects of various drugs across a heterogeneous population of diseased individuals. Both EN.580.488 and EN.580.430 require a background in linear algebra, differential equations, probability, and statistics.

In addition to the elective requirements, students with a declared Computational Medicine minor are REQUIRED to attend no less than 6 ICM Distinguished Seminars in person prior to graduation. Documentation of seminar attendance is two-fold: (1) Students must sign-in at every seminar attended and (2) students must complete the online Seminar Attendance Form. Please note that undergraduates do not need to register for the Distinguished Seminar Series in Computational Medicine course (EN.580.736/7) but do need to attend six ICM seminars and document their attendance to graduate with a Computational Medicine minor.

More information on seminar speakers, dates, and topics can be found here.

Following satisfaction of the prerequisites, to complete the minor, an undergraduate must take at least 18 credits of CM courses. This includes two one-semester core courses plus approved elective courses selected from those listed below. The following restrictions apply to elective courses:

No more than 3 of the 18 elective credits can consist of independent research in computational medicine or approved CM-related research. The Senior Design Project Course (EN.580.580/581) may count toward independent research, provided that the research falls within the field of computational medicine, as decided by the advisor. Eligibility of independent research as “M”, “C”, “MC”, or neither is at the advisor’s discretion.

All 18 credits will all be at 300-level or above.

At least 1 non-core course must be outside the student’s home department

At least 2 non-core courses must have a substantial biology or medicine component, as identified in the list below with an “M” designation.

At least 1 non-core course must have a significant component of “applied programming” (distinct from a course on computer language or on programming such as Intermediate Computer Programming in Computer Science) to satisfy the computational component, as identified in the list of electives with an “C” designation.

All courses must be passed at a C- level or above.

A class may not be counted as both a prerequisite and an elective.

Students may suggest elective courses to be added to the list by completing the “Class Approval Request Form”. Requests should be made to Alecia Flynn (aflynn12@jhu.edu) and will be reviewed by the CM Minor Curriculum Committee.

Elective Courses

Significant Biology/Medicine Component (M)

Course #

Department

Course Title

Instructor

Sem

Cr

EN.520.432

ECE

Medical Imaging Systems

Prince

F

3

EN.520.473

ECE

Magnetic Resonance in Medicine*

Bottomley/Schar

S

3

EN.530.676

MechE

Locomotion II: Dynamics

Cowan

S

3

EN.540.400

ChemBE

Project in Design: Pharmacokinetics*

Donohue

F

3

EN.540.421

ChemBE

Project in Design: Pharmacodynamics*

Donohue

S

3

EN.580.430

BME

Systems Pharmacology & Personalized Medicine*

Mac Gabhann

S

4

EN.580.460

BME

Theory of Cancer*

Popel

S

3

EN.580.462

BME

Representations of Choice*

Chib

S

3

EN.580.480

BME

Precision Care Medicine*

Winslow/Sarma

F/S

3

EN.580.488

BME

Foundations of Computational Biology & Bioinformatics II*

Karchin

S

3

EN.580.626

BME

Computational Models of the Cardiac Myocyte*

Winslow

S

3

EN.580.689

BME

Computational Personal Genomics*

Salzberg

S

3

EN.580.694

BME

Statistical Connectomics*

Vogelstein

S

3

EN.580.446

BME

Physical Epigenetics

Feinberg/Ha

S

3

EN.580.420

BME

Build-A-Genome

Bader/Zeller

F

4

EN.580.492

BME

Build-A-Genome Mentor

Bader/Zeller

F

4

EN.601.448

CS

Computational Genomics: Data Analysis*

Battle

S

3

EN.601.447

CS

Computational Genomics: Sequences*

Langmead

F

3

EN.601.350

CS

Introduction to Genomic Research*

Salzberg

S

3

EN.601.750

CS

Frontiers of Sequencing Data Analysis*

Langmead

S

3

AS.250.353

Biophysics

Computational Biology*

Fleming

F

3

Significant Computational Component (C)

Course #

Department

Course Title

Instructor

Sem

Cr

EN.540.409

ChemBE

Dynamic Modeling & Control

Goffin

F

4

EN.520.473

ECE

Magnetic Resonance in Medicine*

Bottomley/Schar

S

3

EN.540.400

ChemBE

Project in Design: Pharmacokinetics*

Donohue

F

3

EN.540.421

ChemBE

Project in Design: Pharmacodynamics*

Donohue

S

3

EN.540.638

ChemBE

Advanced Topics in Pharmacokinetics and Pharmacodynamics I

Donohue

F

3

EN.553.386

AMS

Scientific Computing: Differential Equations

Eyink

S

4

EN.553.492

AMS

Mathematical Biology

Athreya

S

3

EN.553.436

AMS

Data Mining

Budavari

F

4

EN.580.445

BME

Networks

Sarma

F

3

EN.580.468

BME

The Art of Data Science

Vogelstein

S

3

EN.580.480

BME

Precision Care Medicine*

Winslow/Sarma

F/S

3

EN.580.491

BME

Learning Theory

Shadmehr

S

3

EN.580.430

BME

Systems Pharmacology & Personalized Medicine*

Mac Gabhann

S

4

EN.580.460

BME

Theory of Cancer*

Popel

S

3

EN.580.462

BME

Representations of Choice*

Chib

S

3

EN.580.488

BME

Foundations of Computational Biology & Bioinformatics II*

Karchin

S

3

EN.580.682

BME

Computational Models of the Cardiac Myocyte*

Winslow

S

3

EN.580.689

BME

Computational Personal Genomics*

Salzberg

S

3

EN.580.694

BME

Statistical Connectomics*

Vogelstein

S

3

EN.601.323

CS

Data-Intensive Computing

Burns

F

3

EN.601.350

CS

Introduction to Genomic Research*

Salzberg

S

3

EN.601.445

CS

Computer Integrated Surgery 1

Taylor

F

4

EN.601.447

CS

Computational Genomics: Sequences*

Langmead

F

3

EN.601.448

CS

Computational Genomics: Data Analysis*

Battle

S

3

EN.601.461

CS

Computer Vision

Reiter

F

3

EN.601.475

CS

Machine Learning

Staff

S

3

EN.601.476

CS

Machine Learning: Data to Models

Saria

S

3

EN.601.750

CS

Frontiers of Sequencing Data Analysis*

Langmead

S

3

EN.601.482

CS

Machine Learning: Deep Learning

Hager

S

3

EN.601.485

CS

Probabilistic Models of the Visual Cortex

Yuille

F

3

EN.601.723

CS

Advanced Topics in Data-Intensive Computing

Burns

F

3

AS.250.353

Biophysics

Computational Biology*

Fleming

F

3

Other Electives

Course #

Department

Course Title

Instructor

Sem

Cr

EN.520.315

ECE

Introduction to Bio-Inspired Processing of Audio-Visual Signals

Hermansky

F

3

EN.520.601

ECE

Introduction to Linear Systems Theory

Inglesias

S

3

EN.520.621

ECE

Introduction to Nonlinear Systems

Inglesias

S

3

EN.530.343

MechE

Design & Analysis of Dynamical Systems

Cowan

S

3

EN.553.391

AMS

Dynamical Systems

Athavale

F

4

EN.553.420

AMS

Introduction to Probability [if not prereq.]

Torcaso

S

4

EN.553.426

AMS

Introduction to Stochastic Processes

Wierman

S

4

EN.553.430

AMS

Introduction to Statistics [if not prereq.]

Athreya/Naiman

F

4

*May be used to satisfy “C” or “M” requirement but not both.

Legend: F = Fall / S = Spring, Cr = number of credits

Specific questions regarding the minor can be directed to Alecia Flynn, Academic Coordinator for ICM.